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This study aims to evaluate the performance of various deep learning architectures for classifying microseismic events in enhanced geothermal systems.

July 20, 2026Engineering Applications of Artificial IntelligenceOpen Access

Deep learning for distributed acoustic sensing-based microseismic candidate-window classification in enhanced geothermal systems: A comparative study of twelve architectures

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Authors

MRMd Shokor A Rahaman JJJafreezal JaafarFPT University IPIrving Vitra PaputunganIslamic University of Indonesia

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Implication

Comparative study demonstrates deep learning models outperform classical detectors for microseismic discrimination in geothermal monitoring, suggesting improved accuracy.

Key Points

This study aims to evaluate the performance of various deep learning architectures for classifying microseismic events in enhanced geothermal systems.
Conducted a comparative study of twelve deep learning models across five architecture families on 3974 labeled recordings.
Utilized metrics such as F1-score, area under the receiver operating characteristic curve, and balanced accuracy for evaluation.
Performed external testing on a separate dataset to assess model robustness and transferability.
The squeeze-and-excitation residual network achieved an F1-score of 98.50% and an area under the curve of 99.46%.
A hybrid convolutional neural network–gated recurrent unit model scored an F1 of 0.9930 in external testing, indicating strong performance on new data.
Statistical analysis showed no significant difference in performance metrics between top models, confirming robustness despite variations.

Cite This Study

Rahaman et al. (2026) studied this question.

synapsesocial.com/papers/6a5dba718bd453d3397aba18 https://doi.org/10.1016/j.engappai.2026.115622

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Implication

Comparative study demonstrates deep learning models outperform classical detectors for microseismic discrimination in geothermal monitoring, suggesting improved accuracy.

Deep learning for distributed acoustic sensing-based microseismic candidate-window classification in enhanced geothermal systems: A comparative study of twelve architectures

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Cite This Study

Deep learning for distributed acoustic sensing-based microseismic candidate-window classification in enhanced geothermal systems: A comparative study of twelve architectures

Discussion

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Implication

Key Points

Cite This Study

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